Overview

Dataset statistics

Number of variables24
Number of observations367
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory68.9 KiB
Average record size in memory192.3 B

Variable types

Categorical4
Numeric20

Warnings

Date has a high cardinality: 366 distinct values High cardinality
Steps is highly correlated with Distance and 2 other fieldsHigh correlation
Distance is highly correlated with Steps and 2 other fieldsHigh correlation
Minutes Very Active is highly correlated with Steps and 2 other fieldsHigh correlation
MinutesOfSleep is highly correlated with MinutesOfBeingAwake and 4 other fieldsHigh correlation
MinutesOfBeingAwake is highly correlated with MinutesOfSleep and 4 other fieldsHigh correlation
NumberOfAwakings is highly correlated with MinutesOfSleep and 4 other fieldsHigh correlation
LengthOfRestInMinutes is highly correlated with MinutesOfSleep and 4 other fieldsHigh correlation
Distance_miles is highly correlated with Steps and 2 other fieldsHigh correlation
Days_encoded is highly correlated with Work_or_WeekendHigh correlation
Work_or_Weekend is highly correlated with Days_encodedHigh correlation
Hours Sleep is highly correlated with MinutesOfSleep and 4 other fieldsHigh correlation
Sleep efficiency is highly correlated with MinutesOfSleep and 4 other fieldsHigh correlation
Yesterday_sleep is highly correlated with Yesterday_sleep_efficiencyHigh correlation
Yesterday_sleep_efficiency is highly correlated with Yesterday_sleepHigh correlation
Calorie burned is highly correlated with Steps and 3 other fieldsHigh correlation
Steps is highly correlated with Calorie burned and 3 other fieldsHigh correlation
Distance is highly correlated with Calorie burned and 3 other fieldsHigh correlation
Floors is highly correlated with Minutes Very ActiveHigh correlation
Minutes Very Active is highly correlated with Calorie burned and 4 other fieldsHigh correlation
MinutesOfSleep is highly correlated with MinutesOfBeingAwake and 3 other fieldsHigh correlation
MinutesOfBeingAwake is highly correlated with MinutesOfSleep and 3 other fieldsHigh correlation
NumberOfAwakings is highly correlated with MinutesOfSleep and 3 other fieldsHigh correlation
LengthOfRestInMinutes is highly correlated with MinutesOfSleep and 3 other fieldsHigh correlation
Distance_miles is highly correlated with Calorie burned and 3 other fieldsHigh correlation
Days_encoded is highly correlated with Work_or_WeekendHigh correlation
Work_or_Weekend is highly correlated with Days_encodedHigh correlation
Hours Sleep is highly correlated with MinutesOfSleep and 3 other fieldsHigh correlation
Calorie burned is highly correlated with Distance and 1 other fieldsHigh correlation
Steps is highly correlated with Distance and 1 other fieldsHigh correlation
Distance is highly correlated with Calorie burned and 3 other fieldsHigh correlation
Minutes Very Active is highly correlated with Distance and 1 other fieldsHigh correlation
MinutesOfSleep is highly correlated with MinutesOfBeingAwake and 3 other fieldsHigh correlation
MinutesOfBeingAwake is highly correlated with MinutesOfSleep and 3 other fieldsHigh correlation
NumberOfAwakings is highly correlated with MinutesOfSleep and 3 other fieldsHigh correlation
LengthOfRestInMinutes is highly correlated with MinutesOfSleep and 3 other fieldsHigh correlation
Distance_miles is highly correlated with Calorie burned and 3 other fieldsHigh correlation
Days_encoded is highly correlated with Work_or_WeekendHigh correlation
Work_or_Weekend is highly correlated with Days_encodedHigh correlation
Hours Sleep is highly correlated with MinutesOfSleep and 3 other fieldsHigh correlation
Work_or_Weekend is highly correlated with Days_encoded and 2 other fieldsHigh correlation
Hours Sleep is highly correlated with MinutesOfBeingAwake and 4 other fieldsHigh correlation
MinutesOfBeingAwake is highly correlated with Hours Sleep and 4 other fieldsHigh correlation
Minutes Fairly Active is highly correlated with Distance and 3 other fieldsHigh correlation
Months is highly correlated with Months_encodedHigh correlation
Steps is highly correlated with Distance and 5 other fieldsHigh correlation
Distance is highly correlated with Minutes Fairly Active and 7 other fieldsHigh correlation
MinutesOfSleep is highly correlated with Hours Sleep and 4 other fieldsHigh correlation
Yesterday_sleep_efficiency is highly correlated with Steps and 3 other fieldsHigh correlation
Minutes Sedentary is highly correlated with Minutes Fairly Active and 2 other fieldsHigh correlation
Activity Calories is highly correlated with Steps and 4 other fieldsHigh correlation
Days_encoded is highly correlated with Work_or_Weekend and 1 other fieldsHigh correlation
Distance_miles is highly correlated with Minutes Fairly Active and 7 other fieldsHigh correlation
LengthOfRestInMinutes is highly correlated with Hours Sleep and 4 other fieldsHigh correlation
Floors is highly correlated with Work_or_WeekendHigh correlation
Sleep efficiency is highly correlated with Hours Sleep and 4 other fieldsHigh correlation
Minutes Lightly Active is highly correlated with Distance and 4 other fieldsHigh correlation
Yesterday_sleep is highly correlated with Yesterday_sleep_efficiencyHigh correlation
Days is highly correlated with Work_or_Weekend and 1 other fieldsHigh correlation
Calorie burned is highly correlated with Steps and 5 other fieldsHigh correlation
Minutes Very Active is highly correlated with Minutes Fairly Active and 3 other fieldsHigh correlation
Months_encoded is highly correlated with MonthsHigh correlation
NumberOfAwakings is highly correlated with Hours Sleep and 4 other fieldsHigh correlation
Work_or_Weekend is highly correlated with DaysHigh correlation
Days is highly correlated with Work_or_WeekendHigh correlation
Date is uniformly distributed Uniform
Days is uniformly distributed Uniform
Months is uniformly distributed Uniform
Floors has 39 (10.6%) zeros Zeros
Minutes Lightly Active has 4 (1.1%) zeros Zeros
Minutes Fairly Active has 47 (12.8%) zeros Zeros
Minutes Very Active has 49 (13.4%) zeros Zeros
Activity Calories has 4 (1.1%) zeros Zeros
MinutesOfSleep has 57 (15.5%) zeros Zeros
MinutesOfBeingAwake has 58 (15.8%) zeros Zeros
NumberOfAwakings has 58 (15.8%) zeros Zeros
LengthOfRestInMinutes has 57 (15.5%) zeros Zeros
Days_encoded has 52 (14.2%) zeros Zeros
Hours Sleep has 57 (15.5%) zeros Zeros
Sleep efficiency has 57 (15.5%) zeros Zeros
Yesterday_sleep has 58 (15.8%) zeros Zeros
Yesterday_sleep_efficiency has 58 (15.8%) zeros Zeros

Reproduction

Analysis started2021-06-01 16:12:58.224091
Analysis finished2021-06-01 16:13:30.869415
Duration32.65 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct366
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2016-02-05
 
2
2015-09-19
 
1
2016-01-01
 
1
2016-03-09
 
1
2015-07-29
 
1
Other values (361)
361 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3670
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique365 ?
Unique (%)99.5%

Sample

1st row2015-05-08
2nd row2015-05-09
3rd row2015-05-10
4th row2015-05-11
5th row2015-05-12

Common Values

ValueCountFrequency (%)
2016-02-052
 
0.5%
2015-09-191
 
0.3%
2016-01-011
 
0.3%
2016-03-091
 
0.3%
2015-07-291
 
0.3%
2015-10-061
 
0.3%
2016-01-231
 
0.3%
2016-04-211
 
0.3%
2016-02-221
 
0.3%
2015-05-101
 
0.3%
Other values (356)356
97.0%

Length

2021-06-01T18:13:30.999897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-02-052
 
0.5%
2015-09-191
 
0.3%
2016-01-011
 
0.3%
2016-03-091
 
0.3%
2015-07-291
 
0.3%
2015-10-061
 
0.3%
2016-01-231
 
0.3%
2016-04-211
 
0.3%
2016-02-221
 
0.3%
2015-05-101
 
0.3%
Other values (356)356
97.0%

Most occurring characters

ValueCountFrequency (%)
0817
22.3%
-734
20.0%
1683
18.6%
2584
15.9%
5306
 
8.3%
6195
 
5.3%
385
 
2.3%
867
 
1.8%
767
 
1.8%
966
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2936
80.0%
Dash Punctuation734
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0817
27.8%
1683
23.3%
2584
19.9%
5306
 
10.4%
6195
 
6.6%
385
 
2.9%
867
 
2.3%
767
 
2.3%
966
 
2.2%
466
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3670
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0817
22.3%
-734
20.0%
1683
18.6%
2584
15.9%
5306
 
8.3%
6195
 
5.3%
385
 
2.3%
867
 
1.8%
767
 
1.8%
966
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0817
22.3%
-734
20.0%
1683
18.6%
2584
15.9%
5306
 
8.3%
6195
 
5.3%
385
 
2.3%
867
 
1.8%
767
 
1.8%
966
 
1.8%

Calorie burned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct326
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2741.501362
Minimum179
Maximum4351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:31.067220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum179
5-th percentile306.2
Q12698
median2974
Q33233
95-th percentile3626.2
Maximum4351
Range4172
Interquartile range (IQR)535

Descriptive statistics

Standard deviation916.3070359
Coefficient of variation (CV)0.3342354844
Kurtosis2.592310691
Mean2741.501362
Median Absolute Deviation (MAD)267
Skewness-1.827296598
Sum1006131
Variance839618.584
MonotonicityNot monotonic
2021-06-01T18:13:31.137456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3113
 
0.8%
33523
 
0.8%
3093
 
0.8%
30633
 
0.8%
29313
 
0.8%
30722
 
0.5%
31352
 
0.5%
29922
 
0.5%
31682
 
0.5%
29782
 
0.5%
Other values (316)342
93.2%
ValueCountFrequency (%)
1792
0.5%
2421
0.3%
2461
0.3%
2471
0.3%
2591
0.3%
2661
0.3%
2741
0.3%
2751
0.3%
2761
0.3%
2811
0.3%
ValueCountFrequency (%)
43511
0.3%
41971
0.3%
41121
0.3%
40831
0.3%
40611
0.3%
40121
0.3%
38991
0.3%
38011
0.3%
37961
0.3%
37921
0.3%

Steps
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct360
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10121.58856
Minimum0
Maximum26444
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:31.206723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile811
Q16730.5
median10413
Q313916.5
95-th percentile19067.8
Maximum26444
Range26444
Interquartile range (IQR)7186

Descriptive statistics

Standard deviation5594.836225
Coefficient of variation (CV)0.5527626611
Kurtosis-0.3169350864
Mean10121.58856
Median Absolute Deviation (MAD)3642
Skewness0.01604320548
Sum3714623
Variance31302192.38
MonotonicityNot monotonic
2021-06-01T18:13:31.284270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.8%
104342
 
0.5%
145772
 
0.5%
179132
 
0.5%
8522
 
0.5%
10652
 
0.5%
87041
 
0.3%
71071
 
0.3%
172521
 
0.3%
116181
 
0.3%
Other values (350)350
95.4%
ValueCountFrequency (%)
03
0.8%
101
 
0.3%
391
 
0.3%
951
 
0.3%
991
 
0.3%
2191
 
0.3%
4431
 
0.3%
5021
 
0.3%
5651
 
0.3%
6261
 
0.3%
ValueCountFrequency (%)
264441
0.3%
255711
0.3%
253851
0.3%
233131
0.3%
232861
0.3%
222381
0.3%
219131
0.3%
214791
0.3%
213831
0.3%
202691
0.3%

Distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct318
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.549128065
Minimum0
Maximum20.45
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:31.360499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.824
Q16.155
median8.29
Q310.56
95-th percentile14.698
Maximum20.45
Range20.45
Interquartile range (IQR)4.405

Descriptive statistics

Standard deviation3.409880661
Coefficient of variation (CV)0.3988571273
Kurtosis0.5017130549
Mean8.549128065
Median Absolute Deviation (MAD)2.19
Skewness0.4250195875
Sum3137.53
Variance11.62728612
MonotonicityNot monotonic
2021-06-01T18:13:31.449477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.454
 
1.1%
8.664
 
1.1%
03
 
0.8%
9.233
 
0.8%
2.872
 
0.5%
4.832
 
0.5%
6.032
 
0.5%
5.982
 
0.5%
13.662
 
0.5%
14.842
 
0.5%
Other values (308)341
92.9%
ValueCountFrequency (%)
03
0.8%
0.031
 
0.3%
0.651
 
0.3%
1.41
 
0.3%
1.631
 
0.3%
1.871
 
0.3%
2.311
 
0.3%
2.441
 
0.3%
2.791
 
0.3%
2.872
0.5%
ValueCountFrequency (%)
20.451
0.3%
19.651
0.3%
19.31
0.3%
17.841
0.3%
17.321
0.3%
16.691
0.3%
16.521
0.3%
16.41
0.3%
15.981
0.3%
15.511
0.3%

Floors
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct40
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.72479564
Minimum0
Maximum101
Zeros39
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:31.534519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median11
Q316
95-th percentile26
Maximum101
Range101
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.33736979
Coefficient of variation (CV)0.8816673743
Kurtosis18.63567774
Mean11.72479564
Median Absolute Deviation (MAD)5
Skewness2.969491321
Sum4303
Variance106.8612141
MonotonicityNot monotonic
2021-06-01T18:13:31.614595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
039
 
10.6%
1131
 
8.4%
1422
 
6.0%
122
 
6.0%
1020
 
5.4%
1320
 
5.4%
1719
 
5.2%
1517
 
4.6%
216
 
4.4%
1216
 
4.4%
Other values (30)145
39.5%
ValueCountFrequency (%)
039
10.6%
122
6.0%
216
4.4%
37
 
1.9%
46
 
1.6%
57
 
1.9%
612
 
3.3%
77
 
1.9%
815
 
4.1%
912
 
3.3%
ValueCountFrequency (%)
1011
0.3%
641
0.3%
611
0.3%
591
0.3%
521
0.3%
421
0.3%
352
0.5%
342
0.5%
331
0.3%
322
0.5%

Minutes Sedentary
Real number (ℝ≥0)

HIGH CORRELATION

Distinct282
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean563.9344823
Minimum1.002
Maximum998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:31.702288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.002
5-th percentile1.1015
Q1520
median663
Q3756.5
95-th percentile895.3
Maximum998
Range996.998
Interquartile range (IQR)236.5

Descriptive statistics

Standard deviation294.7931449
Coefficient of variation (CV)0.5227436061
Kurtosis-0.1550914445
Mean563.9344823
Median Absolute Deviation (MAD)112
Skewness-1.067336125
Sum206963.955
Variance86902.99827
MonotonicityNot monotonic
2021-06-01T18:13:31.787308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1184
 
1.1%
6694
 
1.1%
7824
 
1.1%
6063
 
0.8%
8023
 
0.8%
1.443
 
0.8%
5193
 
0.8%
7183
 
0.8%
8273
 
0.8%
5613
 
0.8%
Other values (272)334
91.0%
ValueCountFrequency (%)
1.0021
0.3%
1.0131
0.3%
1.0341
0.3%
1.0421
0.3%
1.0431
0.3%
1.0461
0.3%
1.0471
0.3%
1.0511
0.3%
1.0532
0.5%
1.0691
0.3%
ValueCountFrequency (%)
9981
0.3%
9881
0.3%
9851
0.3%
9811
0.3%
9781
0.3%
9761
0.3%
9661
0.3%
9622
0.5%
9541
0.3%
9501
0.3%

Minutes Lightly Active
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct210
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.4059946
Minimum0
Maximum472
Zeros4
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:31.872335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile98.3
Q1179
median226
Q3290
95-th percentile388.7
Maximum472
Range472
Interquartile range (IQR)111

Descriptive statistics

Standard deviation86.5313757
Coefficient of variation (CV)0.3660286866
Kurtosis0.2640237824
Mean236.4059946
Median Absolute Deviation (MAD)55
Skewness0.1238876752
Sum86761
Variance7487.67898
MonotonicityNot monotonic
2021-06-01T18:13:31.957372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2485
 
1.4%
1985
 
1.4%
2105
 
1.4%
2345
 
1.4%
2225
 
1.4%
2834
 
1.1%
1664
 
1.1%
2904
 
1.1%
2474
 
1.1%
1624
 
1.1%
Other values (200)322
87.7%
ValueCountFrequency (%)
04
1.1%
51
 
0.3%
101
 
0.3%
441
 
0.3%
461
 
0.3%
651
 
0.3%
701
 
0.3%
731
 
0.3%
751
 
0.3%
801
 
0.3%
ValueCountFrequency (%)
4721
0.3%
4711
0.3%
4511
0.3%
4391
0.3%
4361
0.3%
4301
0.3%
4291
0.3%
4271
0.3%
4251
0.3%
4211
0.3%

Minutes Fairly Active
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct74
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.16348774
Minimum0
Maximum101
Zeros47
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:32.031557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median24
Q341.5
95-th percentile60
Maximum101
Range101
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation20.31945626
Coefficient of variation (CV)0.776634081
Kurtosis-0.2434308097
Mean26.16348774
Median Absolute Deviation (MAD)16
Skewness0.5624096011
Sum9602
Variance412.8803026
MonotonicityNot monotonic
2021-06-01T18:13:32.333172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
047
 
12.8%
816
 
4.4%
1011
 
3.0%
2810
 
2.7%
528
 
2.2%
38
 
2.2%
98
 
2.2%
258
 
2.2%
158
 
2.2%
367
 
1.9%
Other values (64)236
64.3%
ValueCountFrequency (%)
047
12.8%
23
 
0.8%
38
 
2.2%
46
 
1.6%
55
 
1.4%
65
 
1.4%
76
 
1.6%
816
 
4.4%
98
 
2.2%
1011
 
3.0%
ValueCountFrequency (%)
1011
0.3%
871
0.3%
861
0.3%
791
0.3%
781
0.3%
751
0.3%
731
0.3%
721
0.3%
671
0.3%
661
0.3%

Minutes Very Active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct103
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.72207084
Minimum0
Maximum153
Zeros49
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:32.422624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.5
median29
Q354
95-th percentile95.7
Maximum153
Range153
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation31.00668231
Coefficient of variation (CV)0.8679978952
Kurtosis0.7718863627
Mean35.72207084
Median Absolute Deviation (MAD)22
Skewness0.9756336784
Sum13110
Variance961.4143476
MonotonicityNot monotonic
2021-06-01T18:13:32.512047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
049
 
13.4%
2910
 
2.7%
518
 
2.2%
248
 
2.2%
137
 
1.9%
307
 
1.9%
207
 
1.9%
47
 
1.9%
547
 
1.9%
86
 
1.6%
Other values (93)251
68.4%
ValueCountFrequency (%)
049
13.4%
16
 
1.6%
25
 
1.4%
34
 
1.1%
47
 
1.9%
53
 
0.8%
63
 
0.8%
72
 
0.5%
86
 
1.6%
92
 
0.5%
ValueCountFrequency (%)
1531
0.3%
1401
0.3%
1321
0.3%
1301
0.3%
1291
0.3%
1281
0.3%
1241
0.3%
1231
0.3%
1181
0.3%
1101
0.3%

Activity Calories
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct325
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2044.147139
Minimum0
Maximum9830
Zeros4
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:32.600366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile139.9
Q11218.5
median1553
Q31927.5
95-th percentile7871
Maximum9830
Range9830
Interquartile range (IQR)709

Descriptive statistics

Standard deviation2041.267168
Coefficient of variation (CV)0.9985911136
Kurtosis5.392666339
Mean2044.147139
Median Absolute Deviation (MAD)347
Skewness2.446438269
Sum750202
Variance4166771.65
MonotonicityNot monotonic
2021-06-01T18:13:32.690336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04
 
1.1%
1534
 
1.1%
17663
 
0.8%
14193
 
0.8%
15642
 
0.5%
15442
 
0.5%
10922
 
0.5%
14382
 
0.5%
19472
 
0.5%
21552
 
0.5%
Other values (315)341
92.9%
ValueCountFrequency (%)
04
1.1%
141
 
0.3%
162
0.5%
221
 
0.3%
271
 
0.3%
1021
 
0.3%
1031
 
0.3%
1051
 
0.3%
1111
 
0.3%
1141
 
0.3%
ValueCountFrequency (%)
98301
0.3%
96202
0.5%
95801
0.3%
95601
0.3%
93901
0.3%
91401
0.3%
89001
0.3%
88201
0.3%
88101
0.3%
87801
0.3%

MinutesOfSleep
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct206
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290.479564
Minimum0
Maximum553
Zeros57
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:32.773923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1224
median337
Q3400.5
95-th percentile470.8
Maximum553
Range553
Interquartile range (IQR)176.5

Descriptive statistics

Standard deviation154.7523281
Coefficient of variation (CV)0.5327477291
Kurtosis-0.4928343775
Mean290.479564
Median Absolute Deviation (MAD)74
Skewness-0.7998062555
Sum106606
Variance23948.28305
MonotonicityNot monotonic
2021-06-01T18:13:32.846928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057
 
15.5%
3885
 
1.4%
3475
 
1.4%
3685
 
1.4%
4074
 
1.1%
3794
 
1.1%
3834
 
1.1%
3163
 
0.8%
3763
 
0.8%
4643
 
0.8%
Other values (196)274
74.7%
ValueCountFrequency (%)
057
15.5%
521
 
0.3%
541
 
0.3%
641
 
0.3%
691
 
0.3%
761
 
0.3%
771
 
0.3%
782
 
0.5%
801
 
0.3%
921
 
0.3%
ValueCountFrequency (%)
5531
0.3%
5511
0.3%
5501
0.3%
5441
0.3%
5411
0.3%
5291
0.3%
5261
0.3%
5251
0.3%
5241
0.3%
5181
0.3%

MinutesOfBeingAwake
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct68
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.00817439
Minimum0
Maximum78
Zeros58
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:32.933900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median29
Q341.5
95-th percentile57
Maximum78
Range78
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation18.54141543
Coefficient of variation (CV)0.6620001422
Kurtosis-0.7746498319
Mean28.00817439
Median Absolute Deviation (MAD)14
Skewness0.04849172214
Sum10279
Variance343.784086
MonotonicityNot monotonic
2021-06-01T18:13:33.023982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058
 
15.8%
3114
 
3.8%
3513
 
3.5%
3912
 
3.3%
2911
 
3.0%
4110
 
2.7%
4610
 
2.7%
339
 
2.5%
268
 
2.2%
228
 
2.2%
Other values (58)214
58.3%
ValueCountFrequency (%)
058
15.8%
22
 
0.5%
31
 
0.3%
41
 
0.3%
52
 
0.5%
63
 
0.8%
73
 
0.8%
84
 
1.1%
93
 
0.8%
104
 
1.1%
ValueCountFrequency (%)
781
 
0.3%
751
 
0.3%
721
 
0.3%
671
 
0.3%
662
0.5%
653
0.8%
631
 
0.3%
622
0.5%
612
0.5%
601
 
0.3%

NumberOfAwakings
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.19618529
Minimum0
Maximum45
Zeros58
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:33.108895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median16
Q324
95-th percentile33
Maximum45
Range45
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.75762211
Coefficient of variation (CV)0.6642071526
Kurtosis-0.881591272
Mean16.19618529
Median Absolute Deviation (MAD)8
Skewness0.03982305538
Sum5944
Variance115.7264335
MonotonicityNot monotonic
2021-06-01T18:13:33.173912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
058
 
15.8%
1415
 
4.1%
2115
 
4.1%
1514
 
3.8%
1614
 
3.8%
1713
 
3.5%
2713
 
3.5%
2313
 
3.5%
2612
 
3.3%
2212
 
3.3%
Other values (32)188
51.2%
ValueCountFrequency (%)
058
15.8%
11
 
0.3%
22
 
0.5%
33
 
0.8%
43
 
0.8%
58
 
2.2%
67
 
1.9%
711
 
3.0%
87
 
1.9%
97
 
1.9%
ValueCountFrequency (%)
451
 
0.3%
441
 
0.3%
391
 
0.3%
382
 
0.5%
372
 
0.5%
361
 
0.3%
353
 
0.8%
344
1.1%
338
2.2%
323
 
0.8%

LengthOfRestInMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct206
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321.3433243
Minimum0
Maximum607
Zeros57
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:33.258986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1248
median370
Q3440.5
95-th percentile520.4
Maximum607
Range607
Interquartile range (IQR)192.5

Descriptive statistics

Standard deviation170.7867262
Coefficient of variation (CV)0.5314774365
Kurtosis-0.5035484727
Mean321.3433243
Median Absolute Deviation (MAD)84
Skewness-0.8197264879
Sum117933
Variance29168.10585
MonotonicityNot monotonic
2021-06-01T18:13:33.339016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057
 
15.5%
4367
 
1.9%
4275
 
1.4%
4305
 
1.4%
4205
 
1.4%
3485
 
1.4%
4144
 
1.1%
4684
 
1.1%
4383
 
0.8%
4313
 
0.8%
Other values (196)269
73.3%
ValueCountFrequency (%)
057
15.5%
692
 
0.5%
711
 
0.3%
771
 
0.3%
811
 
0.3%
851
 
0.3%
871
 
0.3%
922
 
0.5%
991
 
0.3%
1061
 
0.3%
ValueCountFrequency (%)
6071
0.3%
6031
0.3%
5941
0.3%
5931
0.3%
5921
0.3%
5831
0.3%
5721
0.3%
5631
0.3%
5562
0.5%
5531
0.3%

Distance_miles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct318
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.312180255
Minimum0
Maximum12.70703695
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:33.424070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.376122704
Q13.824538505
median5.15116559
Q36.56167776
95-th percentile9.132910958
Maximum12.70703695
Range12.70703695
Interquartile range (IQR)2.737139255

Descriptive statistics

Standard deviation2.118800956
Coefficient of variation (CV)0.3988571273
Kurtosis0.5017130549
Mean5.312180255
Median Absolute Deviation (MAD)1.36080249
Skewness0.4250195875
Sum1949.570154
Variance4.489317492
MonotonicityNot monotonic
2021-06-01T18:13:33.499110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.629213954
 
1.1%
5.381072864
 
1.1%
03
 
0.8%
5.735254333
 
0.8%
3.815217942
 
0.5%
1.783334772
 
0.5%
4.063766342
 
0.5%
5.536415612
 
0.5%
5.505347062
 
0.5%
4.343383292
 
0.5%
Other values (308)341
92.9%
ValueCountFrequency (%)
03
0.8%
0.018641131
 
0.3%
0.403891151
 
0.3%
0.86991941
 
0.3%
1.012834731
 
0.3%
1.161963771
 
0.3%
1.435367011
 
0.3%
1.516145241
 
0.3%
1.733625091
 
0.3%
1.783334772
0.5%
ValueCountFrequency (%)
12.707036951
0.3%
12.209940151
0.3%
11.99246031
0.3%
11.085258641
0.3%
10.762145721
0.3%
10.370681991
0.3%
10.265048921
0.3%
10.19048441
0.3%
9.929508581
0.3%
9.637464211
0.3%

Days
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Friday
54 
Saturday
53 
Thursday
52 
Tuesday
52 
Wednesday
52 
Other values (2)
104 

Length

Max length9
Median length7
Mean length7.138964578
Min length6

Characters and Unicode

Total characters2620
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowSaturday
3rd rowSunday
4th rowMonday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Friday54
14.7%
Saturday53
14.4%
Thursday52
14.2%
Tuesday52
14.2%
Wednesday52
14.2%
Sunday52
14.2%
Monday52
14.2%

Length

2021-06-01T18:13:33.649274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:13:33.694700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
friday54
14.7%
saturday53
14.4%
sunday52
14.2%
wednesday52
14.2%
monday52
14.2%
tuesday52
14.2%
thursday52
14.2%

Most occurring characters

ValueCountFrequency (%)
a420
16.0%
d419
16.0%
y367
14.0%
u209
8.0%
r159
 
6.1%
n156
 
6.0%
e156
 
6.0%
s156
 
6.0%
S105
 
4.0%
T104
 
4.0%
Other values (7)369
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2253
86.0%
Uppercase Letter367
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a420
18.6%
d419
18.6%
y367
16.3%
u209
9.3%
r159
 
7.1%
n156
 
6.9%
e156
 
6.9%
s156
 
6.9%
i54
 
2.4%
t53
 
2.4%
Other values (2)104
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S105
28.6%
T104
28.3%
F54
14.7%
M52
14.2%
W52
14.2%

Most occurring scripts

ValueCountFrequency (%)
Latin2620
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a420
16.0%
d419
16.0%
y367
14.0%
u209
8.0%
r159
 
6.1%
n156
 
6.0%
e156
 
6.0%
s156
 
6.0%
S105
 
4.0%
T104
 
4.0%
Other values (7)369
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a420
16.0%
d419
16.0%
y367
14.0%
u209
8.0%
r159
 
6.1%
n156
 
6.0%
e156
 
6.0%
s156
 
6.0%
S105
 
4.0%
T104
 
4.0%
Other values (7)369
14.1%

Days_encoded
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.010899183
Minimum0
Maximum6
Zeros52
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:33.759182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.998603613
Coefficient of variation (CV)0.6637896163
Kurtosis-1.247355133
Mean3.010899183
Median Absolute Deviation (MAD)2
Skewness-0.01300878745
Sum1105
Variance3.994416402
MonotonicityNot monotonic
2021-06-01T18:13:33.822645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
454
14.7%
553
14.4%
052
14.2%
252
14.2%
352
14.2%
152
14.2%
652
14.2%
ValueCountFrequency (%)
052
14.2%
152
14.2%
252
14.2%
352
14.2%
454
14.7%
553
14.4%
652
14.2%
ValueCountFrequency (%)
652
14.2%
553
14.4%
454
14.7%
352
14.2%
252
14.2%
152
14.2%
052
14.2%

Work_or_Weekend
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
1
262 
0
105 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters367
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1262
71.4%
0105
28.6%

Length

2021-06-01T18:13:33.977666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T18:13:34.014064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1262
71.4%
0105
28.6%

Most occurring characters

ValueCountFrequency (%)
1262
71.4%
0105
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number367
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1262
71.4%
0105
28.6%

Most occurring scripts

ValueCountFrequency (%)
Common367
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1262
71.4%
0105
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII367
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1262
71.4%
0105
28.6%

Hours Sleep
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct206
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.841326067
Minimum0
Maximum9.216666667
Zeros57
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:34.060749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.733333333
median5.616666667
Q36.675
95-th percentile7.846666667
Maximum9.216666667
Range9.216666667
Interquartile range (IQR)2.941666667

Descriptive statistics

Standard deviation2.579205468
Coefficient of variation (CV)0.5327477291
Kurtosis-0.4928343775
Mean4.841326067
Median Absolute Deviation (MAD)1.233333333
Skewness-0.7998062555
Sum1776.766667
Variance6.652300848
MonotonicityNot monotonic
2021-06-01T18:13:34.131219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057
 
15.5%
6.4666666675
 
1.4%
6.1333333335
 
1.4%
5.7833333335
 
1.4%
6.3166666674
 
1.1%
6.3833333334
 
1.1%
6.7833333334
 
1.1%
6.63
 
0.8%
6.2666666673
 
0.8%
4.4833333333
 
0.8%
Other values (196)274
74.7%
ValueCountFrequency (%)
057
15.5%
0.86666666671
 
0.3%
0.91
 
0.3%
1.0666666671
 
0.3%
1.151
 
0.3%
1.2666666671
 
0.3%
1.2833333331
 
0.3%
1.32
 
0.5%
1.3333333331
 
0.3%
1.5333333331
 
0.3%
ValueCountFrequency (%)
9.2166666671
0.3%
9.1833333331
0.3%
9.1666666671
0.3%
9.0666666671
0.3%
9.0166666671
0.3%
8.8166666671
0.3%
8.7666666671
0.3%
8.751
0.3%
8.7333333331
0.3%
8.6333333331
0.3%

Sleep efficiency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct293
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.362799
Minimum0
Maximum100
Zeros57
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:34.202943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q186.23853211
median89.43396226
Q392.43841867
95-th percentile95.50212076
Maximum100
Range100
Interquartile range (IQR)6.199886556

Descriptive statistics

Standard deviation32.97319424
Coefficient of variation (CV)0.4317965641
Kurtosis1.568451847
Mean76.362799
Median Absolute Deviation (MAD)3.09516445
Skewness-1.866907636
Sum28025.14723
Variance1087.231538
MonotonicityNot monotonic
2021-06-01T18:13:34.276802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057
 
15.5%
88.888888893
 
0.8%
88.571428572
 
0.5%
86.060606062
 
0.5%
88.652482272
 
0.5%
84.552845532
 
0.5%
88.295165392
 
0.5%
87.393162392
 
0.5%
94.444444442
 
0.5%
88.842975212
 
0.5%
Other values (283)291
79.3%
ValueCountFrequency (%)
057
15.5%
62.068965521
 
0.3%
75.362318841
 
0.3%
82.608695651
 
0.3%
82.892057031
 
0.3%
82.962962961
 
0.3%
83.333333331
 
0.3%
83.478260871
 
0.3%
83.760683761
 
0.3%
83.863636361
 
0.3%
ValueCountFrequency (%)
1001
0.3%
97.183098591
0.3%
97.101449281
0.3%
97.018348621
0.3%
96.726190481
0.3%
96.571428571
0.3%
96.517412941
0.3%
96.29629631
0.3%
96.261682241
0.3%
96.246648791
0.3%

Yesterday_sleep
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct205
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.81852861
Minimum0
Maximum9.216666667
Zeros58
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:34.349357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.725
median5.6
Q36.65
95-th percentile7.785
Maximum9.216666667
Range9.216666667
Interquartile range (IQR)2.925

Descriptive statistics

Standard deviation2.58492994
Coefficient of variation (CV)0.5364562814
Kurtosis-0.5193761113
Mean4.81852861
Median Absolute Deviation (MAD)1.25
Skewness-0.7929176451
Sum1768.4
Variance6.681862796
MonotonicityNot monotonic
2021-06-01T18:13:34.420948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058
 
15.8%
6.4666666675
 
1.4%
5.7833333335
 
1.4%
6.1333333335
 
1.4%
6.3166666674
 
1.1%
6.7833333334
 
1.1%
6.3833333334
 
1.1%
7.1666666673
 
0.8%
4.4833333333
 
0.8%
5.4333333333
 
0.8%
Other values (195)273
74.4%
ValueCountFrequency (%)
058
15.8%
0.86666666671
 
0.3%
0.91
 
0.3%
1.0666666671
 
0.3%
1.151
 
0.3%
1.2666666671
 
0.3%
1.2833333331
 
0.3%
1.32
 
0.5%
1.3333333331
 
0.3%
1.5333333331
 
0.3%
ValueCountFrequency (%)
9.2166666671
0.3%
9.1833333331
0.3%
9.1666666671
0.3%
9.0666666671
0.3%
9.0166666671
0.3%
8.8166666671
0.3%
8.7666666671
0.3%
8.751
0.3%
8.7333333331
0.3%
8.6333333331
0.3%

Yesterday_sleep_efficiency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct292
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.11984209
Minimum0
Maximum100
Zeros58
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:34.493682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q186.23367283
median89.43396226
Q392.43841867
95-th percentile95.50212076
Maximum100
Range100
Interquartile range (IQR)6.204745832

Descriptive statistics

Standard deviation33.20627926
Coefficient of variation (CV)0.4362368386
Kurtosis1.463703709
Mean76.11984209
Median Absolute Deviation (MAD)3.103351169
Skewness-1.839366776
Sum27935.98205
Variance1102.656983
MonotonicityNot monotonic
2021-06-01T18:13:34.576695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058
 
15.8%
88.888888893
 
0.8%
93.49397592
 
0.5%
86.060606062
 
0.5%
88.652482272
 
0.5%
88.295165392
 
0.5%
84.552845532
 
0.5%
87.393162392
 
0.5%
88.842975212
 
0.5%
94.736842112
 
0.5%
Other values (282)290
79.0%
ValueCountFrequency (%)
058
15.8%
62.068965521
 
0.3%
75.362318841
 
0.3%
82.608695651
 
0.3%
82.892057031
 
0.3%
82.962962961
 
0.3%
83.333333331
 
0.3%
83.478260871
 
0.3%
83.760683761
 
0.3%
83.863636361
 
0.3%
ValueCountFrequency (%)
1001
0.3%
97.183098591
0.3%
97.101449281
0.3%
97.018348621
0.3%
96.726190481
0.3%
96.571428571
0.3%
96.517412941
0.3%
96.29629631
0.3%
96.261682241
0.3%
96.246648791
0.3%

Months
Categorical

HIGH CORRELATION
UNIFORM

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
July
31 
May
31 
March
31 
January
31 
October
31 
Other values (7)
212 

Length

Max length9
Median length6
Mean length6.158038147
Min length3

Characters and Unicode

Total characters2260
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMay
2nd rowMay
3rd rowMay
4th rowMay
5th rowMay

Common Values

ValueCountFrequency (%)
July31
8.4%
May31
8.4%
March31
8.4%
January31
8.4%
October31
8.4%
August31
8.4%
December31
8.4%
September30
8.2%
April30
8.2%
November30
8.2%
Other values (2)60
16.3%

Length

2021-06-01T18:13:34.732006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
march31
8.4%
july31
8.4%
may31
8.4%
october31
8.4%
december31
8.4%
january31
8.4%
august31
8.4%
february30
8.2%
april30
8.2%
november30
8.2%
Other values (2)60
16.3%

Most occurring characters

ValueCountFrequency (%)
e334
14.8%
r274
12.1%
u184
 
8.1%
a154
 
6.8%
b152
 
6.7%
y123
 
5.4%
c93
 
4.1%
J92
 
4.1%
t92
 
4.1%
m91
 
4.0%
Other values (16)671
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1893
83.8%
Uppercase Letter367
 
16.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e334
17.6%
r274
14.5%
u184
9.7%
a154
8.1%
b152
8.0%
y123
 
6.5%
c93
 
4.9%
t92
 
4.9%
m91
 
4.8%
n61
 
3.2%
Other values (8)335
17.7%
Uppercase Letter
ValueCountFrequency (%)
J92
25.1%
M62
16.9%
A61
16.6%
O31
 
8.4%
D31
 
8.4%
S30
 
8.2%
N30
 
8.2%
F30
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin2260
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e334
14.8%
r274
12.1%
u184
 
8.1%
a154
 
6.8%
b152
 
6.7%
y123
 
5.4%
c93
 
4.1%
J92
 
4.1%
t92
 
4.1%
m91
 
4.0%
Other values (16)671
29.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e334
14.8%
r274
12.1%
u184
 
8.1%
a154
 
6.8%
b152
 
6.7%
y123
 
5.4%
c93
 
4.1%
J92
 
4.1%
t92
 
4.1%
m91
 
4.0%
Other values (16)671
29.7%

Months_encoded
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.501362398
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2021-06-01T18:13:34.787372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.5
median7
Q39.5
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.459267002
Coefficient of variation (CV)0.5320833989
Kurtosis-1.215084661
Mean6.501362398
Median Absolute Deviation (MAD)3
Skewness-0.001179698595
Sum2386
Variance11.96652819
MonotonicityNot monotonic
2021-06-01T18:13:34.852081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
131
8.4%
331
8.4%
531
8.4%
731
8.4%
831
8.4%
1031
8.4%
1231
8.4%
230
8.2%
430
8.2%
630
8.2%
Other values (2)60
16.3%
ValueCountFrequency (%)
131
8.4%
230
8.2%
331
8.4%
430
8.2%
531
8.4%
630
8.2%
731
8.4%
831
8.4%
930
8.2%
1031
8.4%
ValueCountFrequency (%)
1231
8.4%
1130
8.2%
1031
8.4%
930
8.2%
831
8.4%
731
8.4%
630
8.2%
531
8.4%
430
8.2%
331
8.4%

Interactions

2021-06-01T18:13:06.644420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:06.711274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:06.766094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:06.904372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:06.958731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.013511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.067063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.121652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.180675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.235307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.289572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.348081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.401020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.457846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.513966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.569202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.621629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.675906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.728365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.783114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.836867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.891369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:07.946971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.007266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.062677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.118658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.173390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.229305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.290558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.347463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.403117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.463425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.517656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.575337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.632052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.756045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.810386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.866489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.920830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:08.977141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.032963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.094865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.157467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.224430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.286243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.349688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.411660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.475562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.542774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.606133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.669016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.736131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.797610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.862163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.925999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:09.989365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.049820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.111949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.172607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.236052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.298295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.352210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.407519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.467323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.521703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.576853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.630820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.686017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.745512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.801134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.858001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:10.918253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.053400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.111337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.167960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.224658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.277837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.333338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.387023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.442652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.498031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.552957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.608754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.669495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.724416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.780645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.835786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.891922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:11.952491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.008986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.065296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.125997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.180800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.239054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.295994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.352753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.406479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.462055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.516400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.573107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.628720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.681493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.735669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.794329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.848068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.902525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:12.955355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.009565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.068289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.123058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.177241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.236280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.289033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.345611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.400994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.456188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.508747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.562988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.615871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.670780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.819599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.874853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.931621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:13.992203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.048198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.104559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.159813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.216193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.276624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.333336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.389644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.450496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.505453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.563757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.620774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.677893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.732258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.788343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.842828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.899364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:14.955294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.016474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.079092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.145924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.207764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.270301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.331787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.394409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.460941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.524061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.586720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.653372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.714489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.779641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.843306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.906807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:15.967423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.029730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.090501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.153565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.215210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.270187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.327451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.388302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.444287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.501038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.556148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.612480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.673107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.729612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.785910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.846633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.901462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:16.960024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.017299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.074506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.245823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.302367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.357196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.413776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.469819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.525222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.581223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.641419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.696975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.752982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.807922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.864110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.924493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:17.981014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.036687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.097111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.151679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.209780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.267366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.326554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.381005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.437122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.491560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.548194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.603789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.665247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.727698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.794460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.856636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.919772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:18.981231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.043831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.110706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.173444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.235870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.302410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.363442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.428131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.491725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.555464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.615777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.678184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.738773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.801391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.863287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.915727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:19.969895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.028487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.082274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.136833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.189898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.244106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.302461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.356819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.410805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.468936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.521124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.577682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.632781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.687969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.740077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.794238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.846851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.901289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:20.955185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.012528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.072118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.135251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.193162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.251932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.449333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.508349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.571538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.630750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.689278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.751866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.808875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.869610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.929429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:21.989065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.046148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.104799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.162216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.221432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.279959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.336454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.393787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.456016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.512763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.571055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.627963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.686113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.748561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.806790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.864900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.927198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:22.983653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.043757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.102001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.160403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.215982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.273401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.329263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.387480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.445023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.501528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.559012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.621355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.678111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.736449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.793417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.851383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.913949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:23.972392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.030576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.092862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.149380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.209189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.267824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.325876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.381478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.438842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.494730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.553290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.610915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.663582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.716513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.774787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.827169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.880757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.933456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:24.987028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.045219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.099129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.152636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.210842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.263274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.318878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.373057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.427017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.478017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.530968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.582545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.636945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.690178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.745057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.800898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.861546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.916909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:25.973636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.029262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.085891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.146368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.203051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.259202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.319801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.542435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.600838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.657844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.715681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.770294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.826571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.881406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.938836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:26.995409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.049627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.104070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.163423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.216931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.271717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.325759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.380719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.440172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.496671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.551697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.610435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.663615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.720067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.775449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.830673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.882806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.937425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:27.990016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.045563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.100469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.156932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.214328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.276477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.332744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.389714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.445696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.502955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.564105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.621509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.678680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.740324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.796382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.855497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.913913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:28.971914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.026390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.083810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.138845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.196642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.253600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.309352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.366659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.428056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.484494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.542949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.599218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.655219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.715008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.771214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.826848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.887108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:29.941863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:30.000828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:30.059419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:30.118182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:30.172654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:30.229088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:30.284319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T18:13:30.341554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-01T18:13:34.924211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-01T18:13:35.086678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-01T18:13:35.245565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-01T18:13:35.407968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-01T18:13:35.548527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-01T18:13:30.482351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-01T18:13:30.771533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateCalorie burnedStepsDistanceFloorsMinutes SedentaryMinutes Lightly ActiveMinutes Fairly ActiveMinutes Very ActiveActivity CaloriesMinutesOfSleepMinutesOfBeingAwakeNumberOfAwakingsLengthOfRestInMinutesDistance_milesDaysDays_encodedWork_or_WeekendHours SleepSleep efficiencyYesterday_sleepYesterday_sleep_efficiencyMonthsMonths_encoded
02015-05-0819349050.6501.3554600168038426234170.403891Friday4.016.40000092.0863310.0000000.000000May5
12015-05-0936311892514.114611.0003166160224845435214918.767545Saturday5.007.56666792.4643586.40000092.086331May5
22015-05-1032041422810.571602.0002261477171938746254366.567891Sunday6.006.45000088.7614687.56666792.464358May5
32015-05-11267367565.028749.000190234962031131213503.119282Monday0.015.18333388.8571436.45000088.761468May5
42015-05-1224955023.731876.00017100736040765444912.317714Tuesday1.016.78333382.8920575.18333388.857143May5
52015-05-132767795.7915726.0001723418109440547314573.597738Wednesday2.016.75000088.6214446.78333382.892057May5
62015-05-14268756144.172782.000216131983038128124152.591117Thursday3.016.35000091.8072296.75000088.621444May5
72015-05-15279381696.0714801.00021887109226934163063.771722Friday4.014.48333387.9084976.35000091.807229May5
82015-05-1640611997114.8418532.0002471011182745226185079.221146Saturday5.007.53333389.1518744.48333387.908497May5
92015-05-1733498526.3312606.0001864673176626917112913.933278Sunday6.004.48333392.4398637.53333389.151874May5

Last rows

DateCalorie burnedStepsDistanceFloorsMinutes SedentaryMinutes Lightly ActiveMinutes Fairly ActiveMinutes Very ActiveActivity CaloriesMinutesOfSleepMinutesOfBeingAwakeNumberOfAwakingsLengthOfRestInMinutesDistance_milesDaysDays_encodedWork_or_WeekendHours SleepSleep efficiencyYesterday_sleepYesterday_sleep_efficiencyMonthsMonths_encoded
3572016-04-284032557119.3015606.0293421292711374563443011.992460Thursday3.016.23333386.9767446.01666788.048780April4
3582016-04-2934421752813.3615594.023988219313482093688.301517Friday4.015.80000094.5652176.23333386.976744April4
3592016-04-30306198317.301561.041400165145448275024.536008Saturday5.007.56666790.4382475.80000094.565217April4
3602016-05-0133311426210.605666.03612141193734741203886.586533Sunday6.005.78333389.4329907.56666790.438247May5
3612016-05-022989132369.9812770.0234838147731434233486.201283Monday0.015.23333390.2298855.78333389.432990May5
3622016-05-0337961858814.1316599.0275497923637733184108.779972Tuesday1.016.28333391.9512205.23333390.229885May5
3632016-05-0435251638212.3916684.0333105520754062184277.698787Wednesday2.016.76666795.0819676.28333391.951220May5
3642016-05-0536492191316.4019701.028729902249280351531510.190484Thursday3.014.66666788.8888896.76666795.081967May5
3652016-05-0635391902314.7915575.0298885211237042224129.190077Friday4.016.16666789.8058254.66666788.888889May5
3662016-05-0730495267.082564.03701310160450257315634.399307Saturday5.008.36666789.1651876.16666789.805825May5